Most
of the recent work published in the field of Docking and Scoring
Protein/Ligand complexes have focused in ranking true positives
resulting from a Virtual Library Screening (VLS) through
the use of a specified or consensus linear scoring function.

GFscore is a methodology to accelerate the process
of High Throughput Screening (HTS). We have extended the principle
of consensus scoring in a Non-Linear Neural
Network manner. This original global scoring function is
a combination of the five scoring functions found in the Cscore
package from Tripos Inc.

GFscore learned to discriminate true negatives from false
negatives in a dataset of diverse chemical compounds
and eliminates up to 75% of molecules with a confidence
rate of 90%. The final result is a Hit Enrichment
in the list of molecules to investigate during a research campaign
for biological active compounds. The resulting 25% of molecules
to be tested by in vitro screening make of GFscore a powerful
tool for the biologist, saving his time and money.